Classification of Hand Motion Using Surface EMG Signals

2017 ◽  
pp. 219-238
Author(s):  
Tang Xueyan ◽  
Liu Yunhui ◽  
Lu Congyi ◽  
Poon Weilun
Keyword(s):  
Surface Emg ◽  
Hand Motion

Entropy analysis of surface EMG for classification of face movements

2015 ◽  
Author(s):  
Cagdas Topcu ◽  
Arzu Akgul ◽  
Merve Bedeloglu ◽  
Ela Naz Doger ◽  
Refik Sever ◽  
...  
Keyword(s):  
Surface Emg ◽  
Entropy Analysis

Classification of Simultaneous, Dynamic Motions with Surface EMG

2013 ◽  
pp. 49-52
Author(s):  
Jakob C. Rosenvang ◽  
Ronnie W. Horup ◽  
Kevin Englehart ◽  
Winnie Jensen ◽  
Ernest N. Kamavuako
Keyword(s):  
Surface Emg

Surface EMG and Upper-Limb Rehabilitation

2011 ◽  
pp. 335-343
Author(s):  
Kazuya Funada ◽  
Jinglong Wu ◽  
Satoshi Takahashi
Keyword(s):  
Experimental System ◽  
Surface Emg ◽  
Passive Movement ◽  
Measuring Device ◽  
Upper Limb Rehabilitation ◽  
Hand Motion ◽  
Characteristic Points ◽  
Opening And Closing ◽  
Limb Rehabilitation ◽  
Simple Measuring

In rehabilitating hemiplegic patients, purposeful movements such as the opening and closing of hands are reported to be more effective than passive movement with an instrument. The authors of this chapter used surface electromyogram (surface EMG) signals as a way to convey the patient’s conscious ability to open and close their hands. The muscles in the forearm contract when the hand is closed or opened, which creates a simple signal that is comparatively easy to measure with surface EMG, a simple measuring device. The action potentials of the muscles involved in the opening-and-closing motions of hands were measured from several points in the forearm when those muscles contracted, and their distribution was analyzed. The purpose of this study is to develop a simple system to recognize the movement of a patient’s hand using measurements of EMG signals from only the most characteristic points on the forearm to replace similar, but more complex, research such as multi-channel measurement and wave analysis by FFT. The authors specified the optimum measuring points on the palm and dorsal sides of the forearm for the recognition of hand motion by the experimental system. This system successfully recognized hand motion through the analysis of the surface EMG signals measured from only two optimum points to allow arbitrary control of the rehabilitation device based on the recognition results.

scholarly journals Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pei-Jarn Chen ◽  
Yi-Chun Du
Keyword(s):  
Grey Relational Analysis ◽  
Processing Time ◽  
Back Propagation ◽  
Surface Emg ◽  
Independent Component ◽  
Real Time System ◽  
Data Glove ◽  
Hand Motion ◽  
Relational Analysis ◽  
Grey Relational

This paper proposes a portable system for hand motion identification (HMI) using the features from data glove with bend sensors and multichannel surface electromyography (SEMG). SEMG could provide the information of muscle activities indirectly for HMI. However it is difficult to discriminate the finger motion like extension of thumb and little finger just using SEMG; the data glove with five bend sensors is designed to detect finger motions in the proposed system. Independent component analysis (ICA) and grey relational analysis (GRA) are used to data reduction and the core of identification, respectively. Six features are extracted from each SEMG channel, and three features are computed from five bend sensors in the data glove. To test the feasibility of the system, this study quantitatively compares the classification accuracies of twenty hand motions collected from 10 subjects. Compared to the performance with a back-propagation neural network and only using GRA method, the proposed method provides equivalent accuracy (>85%) with three training sets and faster processing time (20 ms). The results also demonstrate that ICA can effectively reduce the size of input features with GRA methods and, in turn, reduce the processing time with the low price of reduced identification rates.

scholarly journals Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

2010 ◽  
Vol 48 (8) ◽  
pp. 773-781 ◽  
Author(s):  
Rok Istenič ◽  
Prodromos A. Kaplanis ◽  
Constantinos S. Pattichis ◽  
Damjan Zazula
Keyword(s):  
Neuromuscular Disorders ◽  
Surface Emg ◽  
Multiscale Entropy

Classification Of Muscular Strength During Palmar Grasp Exercises Using Surface EMG Signals

2018 ◽  
Author(s):  
Juan C. Yepes ◽  
Alvaro J. Saldarriaga ◽  
Vanessa Montoya-Leal ◽  
Andres Orozco-Duque ◽  
Vera Z. Perez ◽  
...  
Keyword(s):  
Muscular Strength ◽  
Surface Emg
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